Update app.py

app.py

@@ -47,83 +47,105 @@ user_api_key = st.sidebar.text_input(
     type="password")
 
 def ui():
-    
-    
+    user_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
+
     if user_api_key is not None and user_api_key.strip() != "":
-        os.environ["OPENAI_API_KEY"] =user_api_key
+        os.environ["OPENAI_API_KEY"] = user_api_key
         template = """
-            Your custon promp
+            Your custom prompt
             {history}
-            Me:Behave like a  Telecomm customer servce call agent and don't include any website address, compnay name or any other parameter in your output {human_input}
+            Me: Behave like a Telecomm customer service call agent and don't include any website address, company name, or any other parameter in your output {human_input}
             Jack:
-            """
-        
+        """
+
         prompt = PromptTemplate(
-        input_variables=["history", "human_input"],
-        template=template
-    )
-        
+            input_variables=["history", "human_input"],
+            template=template
+        )
+
         llm_chain = LLMChain(
-            llm = ChatOpenAI(temperature=0.0,model_name='gpt-3.5-turbo'),
+            llm=ChatOpenAI(temperature=0.0, model_name='gpt-3.5-turbo'),
             prompt=prompt,
             verbose=True,
             memory=ConversationBufferWindowMemory(k=2)
         )
-    
+
         if 'history' not in st.session_state:
             st.session_state['history'] = []
-    
+
         if 'generated' not in st.session_state:
             st.session_state['generated'] = []
-    
+
         if 'past' not in st.session_state:
             st.session_state['past'] = []
-    
-        if user_api_key is not None and user_api_key.strip() != "":
-            eleven_labs_api_key = st.sidebar.text_input(
-                label="#### Your Eleven Labs API key 👇",
-                placeholder="Paste your Eleven Labs API key",
-                type="password")
-            
-            set_api_key(user_api_key)
-            
-            # #container for the chat history
-            # response_container = st.container()
-            # #container for the user's text input
-            # container = st.container()
-    
-            # with container:
-            with st.form(key='my_form', clear_on_submit=True):
-                audio_file = st.file_uploader("Upload an audio file ", type=[ "wav,Mp4","Mp3"])
-                submit_button = st.form_submit_button(label='Send')
-            if audio_file is not None and submit_button :
-                output_file_path = "./output_audio.mp3"
-                save_uploaded_file_as_mp3(audio_file,output_file_path )
-                hindi_input_audio,sample_rate= librosa.load(output_file_path, sr=None, mono=True)
-                #applying the audio recognition 
-                hindi_transcription=parse_transcription('./output_audio.mp3')
-                st.success(f"Audio file saved as {output_file_path}")
-                #convert hindi to english
-                english_input=hindi_to_english(hindi_transcription)
-                #feeding the input to the LLM
-                english_output = conversational_chat(llm_chain,english_input)
-                #converting english to hindi 
-                hin_output=translate_english_to_hindi(english_output)
-                #getting the hindi_tts 
-                hindi_output_audio=hindi_tts(hin_output)
-                # hindi_output_file="./Hindi_output_Audio.Mp3"
-                # save_uploaded_file_as_mp3(hindi_out"put_audio,hindi_output_file)
-                st.audio(hindi_output_audio)
-
-            #     st.session_state['past'].append(hindi_input_audio)
-            #     st.session_state['generated'].append(hindi_output_audio)
-                
-            # if 'generated' in st.session_state and st.session_state['generated']:
-            #     with response_container:
-            #         for i in range(len(st.session_state['generated'])):
-            #             st.audio(st.session_state["past"][i],format='audio/wav')
-            #             st.audio(st.session_state["generated"][i],format='audio/wav')
 
+        eleven_labs_api_key = st.sidebar.text_input(
+            label="Your Eleven Labs API key 👇",
+            placeholder="Paste your Eleven Labs API key",
+            type="password")
+
+        set_api_key(user_api_key)
+
+        audio_file = st.file_uploader("Upload an audio file", type=["wav", "mp4", "mp3"])
+        if audio_file is not None:
+            output_file_path = "./output_audio.mp3"
+            save_uploaded_file_as_mp3(audio_file, output_file_path)
+            hindi_input_audio, sample_rate = librosa.load(output_file_path, sr=None, mono=True)
+
+            # Applying audio recognition
+            hindi_transcription = parse_transcription('./output_audio.mp3')
+            st.success(f"Audio file saved as {output_file_path}")
+
+            # Convert Hindi to English
+            english_input = hindi_to_english(hindi_transcription)
+
+            # Feeding the input to the LLM
+            english_output = conversational_chat(llm_chain, english_input)
+
+            # Convert English to Hindi
+            hin_output = translate_english_to_hindi(english_output)
+
+            # Getting the Hindi TTS
+            hindi_output_audio = hindi_tts(hin_output)
+
+            # Show original uploaded audio
+            st.audio(audio_file, format='audio/mp3')
+
+            # Show processed output audio
+            st.audio(hindi_output_audio, format='audio/mp3')
+
+    st.markdown("---")
+    # Add a new audio uploader for users to upload another audio file
+    with st.form(key='my_form', clear_on_submit=True):
+        audio_file_new = st.file_uploader("Upload another audio file", type=["wav", "mp4", "mp3"])
+        submit_button = st.form_submit_button(label='Process and Play')
+
+    if audio_file_new is not None and submit_button:
+        output_file_path_new = "./output_audio_new.mp3"
+        save_uploaded_file_as_mp3(audio_file_new, output_file_path_new)
+        hindi_input_audio_new, sample_rate_new = librosa.load(output_file_path_new, sr=None, mono=True)
+
+        # Applying audio recognition for the new file
+        hindi_transcription_new = parse_transcription(output_file_path_new)
+        st.success(f"Audio file saved as {output_file_path_new}")
+
+        # Convert Hindi to English for the new file
+        english_input_new = hindi_to_english(hindi_transcription_new)
+
+        # Feeding the input to the LLM for the new file
+        english_output_new = conversational_chat(llm_chain, english_input_new)
+
+        # Convert English to Hindi for the new file
+        hin_output_new = translate_english_to_hindi(english_output_new)
+
+        # Getting the Hindi TTS for the new file
+        hindi_output_audio_new = hindi_tts(hin_output_new)
+
+        # Show original uploaded audio for the new file
+        st.audio(audio_file_new, format='audio/mp3')
+
+        # Show processed output audio for the new file
+        st.audio(hindi_output_audio_new, format='audio/mp3')
 if __name__ == '__main__':
     ui()